CODEWITHSUNDEEP
pythonnumpyimage-processing
ktom
ktom

Reputation: 145

How to subtract/black-out regions within an image in Python OpenCV

I am working on a project that involves automating a video game using computer vision. My next task involves separating the game's UI elements from the actual game's field of view. For instance:

We would take a screenshot of the entire client window like so: client screenshot

Then we would locate the various UI elements on screen (this is accomplished with template matching, see: Template matching with transparent image templates using OpenCV Python).

Then, we would need to subtract the matched templates from the client screenshot to end up with something like this: client screenshot with UI elements subtracted

This allows me to perform computer vision functions on the game's field of view without the risk of the UI elements interfering.

Let's say we have the following code:

# Let's assume we have already taken a screenshot of the client window
client_window = cv2.imread('client_window.png')

# Here are the three UI areas I'd like to remove/blackout from the above image
ui_chatbox = {'left': 0, 'top': 746, 'width': 520, 'height': 167}
ui_minimap = {'left': 878, 'top': 31, 'width': 212, 'height': 207}
ui_inventory = {'left': 849, 'top': 576, 'width': 241, 'height': 337}

How can we blackout the pixels of the UI element bounding boxes from the client_window matrix?

Upvotes: 1

Views: 392

Answers (1)

ktom
ktom

Reputation: 145

I ended up solving this by using slicing to set all pixels in a range to black. I thought of this because I knew that cropping an image was a similar process, and both involved selecting a range of pixels:

import cv2
import numpy as np

# Let's assume we have already taken a screenshot of the client window
client_window = cv2.imread('client_window.png')
cv2.imshow('client_window', client_window)
cv2.waitKey(0)

# Here are the three UI areas I'd like to remove/blackout from the above image
ui_chatbox = {'left': 0, 'top': 746, 'width': 520, 'height': 167}
ui_minimap = {'left': 878, 'top': 31, 'width': 212, 'height': 207}
ui_inventory = {'left': 849, 'top': 576, 'width': 241, 'height': 337}

client_window[ui_chatbox['top']:ui_chatbox['top'] + ui_chatbox['height'],
                ui_chatbox['left']:ui_chatbox['left'] + ui_chatbox['width']] = np.array([0,0,0])
client_window[ui_minimap['top']:ui_minimap['top'] + ui_minimap['height'],
                ui_minimap['left']:ui_minimap['left'] + ui_minimap['width']] = np.array([0,0,0])
client_window[ui_inventory['top']:ui_inventory['top'] + ui_inventory['height'],
                ui_inventory['left']:ui_inventory['left'] + ui_inventory['width']] = np.array([0,0,0])
cv2.imshow('result', client_window)
cv2.waitKey(0)

Upvotes: 1

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